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Consumption of the Whole-Grain Rye Bread and Progression of Prostate Cancer

., Widmark, A., Johansson, A., Adlercreutz, H., Aman, P., Shepherd, M. J., Hallmans, G. (2000). Rye bran and soy protein delay growth and increase apoptosis of human LNCaP prostate adenocarcinoma in nude mice. Prostate , 42 (4), 304-314. Bylund, A., Lundin, E., Zhang, J. X., Nordin, A., Kaaks, R., Stenman, U. H., Aman, P., Adlercreutz, H., Nilsson, T. K., Hallmans, G., Bergh, A., Stattin P. (2003). Randomised controlled short-term intervention pilot study on rye bran bread in prostate cancer. Eur. J. Cancer. Prev ., 12 (5), 407

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Texture analysis in perfusion images of prostate cancer—A case study

, Investigative Radiology   28 (suppl 5): S72-S77. Bradford, T., Tomlins, S., Wang, X. and Chinnaiyan, A. (2006). Molecular markers of prostate cancer, Urologic Oncology   24 (6): 538-551. Cenic, A., Nabavi, D., Craen, R., Gelb, A. and Lee, T.-Y. (2000). A CT method to measure hemodynamics in brain tumors: Validation and application of cerebral blood flow maps, American Journal of Neuroradiology   21 (3): 462-470. Charlesworth, P. and Harris, A. (2006). Mechanisms of disease: Angiogenesis in

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Data mining methods for gene selection on the basis of gene expression arrays

Mathematics and Computer Science 11(3): 565-582. Tan, P.N., Steinbach, M. and Kumar, V. (2006). Introduction to Data Mining, Pearson Education, Boston, MA. Vanderbilt (2002). Data base of prostate cancer, Vanderbilt University, http://discover1.mc.vanderbilt.edu/discover/public/mcsvm. Vert, J. (2007). Kernel methods in genomics and computational biology, in G. Camps-Valls, J.L. Rojo-Alvarez and M. Martinez-Ramon (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing, Idea Group, London, pp. 42

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Epidemiological, Clinical, Molecular Features and Early Detection Strategy of Most Frequent Hereditary Cancers in Latvia

patients with familial adenomatous polyposis (FAP). Human Mutation: Mutation in Brief , No. 705, http://www3.interscience.wiley.com/homepages/38515/pdf/mutation/705.pdf http://www3.interscience.wiley.com/homepages/38515/pdf/mutation/705.pdf Boltenberg, A., Furgyik, S., Kullander, S. (1990). Familial cancer agregation in cases of adenocarcinoma corporis uteri. Acta Obstet. Gynecol. Scand. , 69 , 249-258. Bratt, O. (2000). Hereditary prostate cancer. BJU Int.   85 (5), 588-98. Bülow, S

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Machine learning techniques combined with dose profiles indicate radiation response biomarkers

., Delobel, J.-B., De Crevoisier, R. and Acosta, O. (2015). On feature extraction and classification in prostate cancer radiotherapy using tensor decompositions, Medical Engineering and Physics 37 (1): 126–131. Finnon, P., Kabacik, S., MacKay, A., Raffy, C., AHern, R., Owen, R., Badie, C., Yarnold, J. and Bouffler, S. (2012). Correlation of in vitro lymphocyte radiosensitivity and gene expression with late normal tissue reactions following curative radiotherapy for breast cancer, Radiotherapy and Oncology 105 (3): 329–336. Francescatto, M., Chierici, M

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Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

, Spain . Yang, Y., Pierce, T. and Carbonell, J.G. (1998). A study of retrospective and on-line event detection, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), Melbourne, Australia , pp. 28-36. Zadeh, L. (1965). Fuzzy sets, Information and Control 8 (3): 338-353. Zupan, B., Demˇsar, J., Kattan, M.W., Beck, J.R. and Bratko, I. (2000). Machine learning for survival analysis: A case study on recurrence of prostate cancer, Artificial

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Fusion of clinical data: A case study to predict the type of treatment of bone fractures

-based data fusion and its application to protein function prediction in yeast, Pacific Symposium on Biocomputing (PSB 2004), Big Island, HI, USA , pp. 300–311. Lee, G., Doyle, S., Monaco, J., Madabhushi, A., Feldman, M.D., Master, S.R. and Tomaszewski, J.E. (2009). A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: Preliminary results in predicting prostate cancer recurrence by fusing mass spectrometry and histology, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA

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From the slit-island method to the Ising model: Analysis of irregular grayscale objects

images of prostate cancer-A case study, International Journal of Applied Mathematics and Computer Science 20(1): 149-156, DOI: 10.2478/v10006-010-0011-9. Solomon, D. and Nayar, R. (2004). The Bethesda System for Reporting Cervical Cytology, Springer, New York, NY. Steven, I. (1993). Linear Richardson plots from non-fractal data sets, Dutch Mathematical Geology 25(6): 737-751. Styer, D. (2007). Statistical Mechanics, Oberlin College, Oberlin. Sun, W. (2006). Three new implementations of the triangular prism

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Nuclei segmentation for computer-aided diagnosis of breast cancer

marching algorithm, Signal Processing: Image Communication 16(10): 963-976. ´Smieta´nski, J., Tadeusiewicz, R. and Łuczy´nska, E. (2010). Texture analysis in perfusion images of prostate cancer- A case study, International Journal of Applied Mathematics and Computer Science 20(1): 149-156, DOI: 10.2478/v10006-010-0011-9. Steć, P. (2005). Segmentation of Colour Video Sequences Using the Fast Marching Method, University of Zielona Góra Press, Zielona Góra. Tang, X. (1998). Texture information in run-length matrices, IEEE Transactions on

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Dynamic Question Ordering in Online Surveys

(2): 271–291. Wang, L., A. Rotnitzky, X. Lin, R.E. Millikan, and P.F. Thall. 2012. “Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer.” Journal of the American Statistical Association 107(498): 493–508. Doi: http://dx.doi.org/10.1080/01621459.2011.641416 . Weisberg, S. 2014. Applied Linear Regression (4th ed.). New York: Wiley. Doi: http://dx.doi.org/10.1002/0471704091 . Weiss, D.J. 1982. “Improving Measurement Quality and Efficiency with Adaptive Testing.” Applied Psychological

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